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- Author or Editor: Kenneth J. Berry x
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Abstract
The Climax I and II wintertime orographic cloud seeding experiments have recently been reanalyzed (Mielke et al., 1981c). The primary inference technique of this recent reanalysis involved (i) target-control residual data resulting from a simple linear model fitted by least squares and (ii) analyses based on classical linear rank statistics. The respective problems associated with this inference technique are (i) the residual data are highly dependent on a few very large values due to the model being fit by least squares and (ii) the complex non-Euclidean geometry underlying classical linear rank tests. The purpose of this article is to describe and apply a new inference technique which resolves both of these problems. This new inference technique involves residual data resulting from a median regression line and analyses based on recently developed rank tests associated with multi-response permutation procedures (MRPP). Application of this new inference to specific meteorological partitions of the Climax I and II experiments indicates that the evidence for a seeding effect is a little stronger (i.e., smaller P values) with the new technique than with the old technique for the warm Climax II 500 mb temperature partition and a Climax I 700 mb wind velocity partition, a little weaker for the warm Climax II 700 mb equivalent potential temperature partition, and about the same for the other meteorological partitions examined.
Abstract
The Climax I and II wintertime orographic cloud seeding experiments have recently been reanalyzed (Mielke et al., 1981c). The primary inference technique of this recent reanalysis involved (i) target-control residual data resulting from a simple linear model fitted by least squares and (ii) analyses based on classical linear rank statistics. The respective problems associated with this inference technique are (i) the residual data are highly dependent on a few very large values due to the model being fit by least squares and (ii) the complex non-Euclidean geometry underlying classical linear rank tests. The purpose of this article is to describe and apply a new inference technique which resolves both of these problems. This new inference technique involves residual data resulting from a median regression line and analyses based on recently developed rank tests associated with multi-response permutation procedures (MRPP). Application of this new inference to specific meteorological partitions of the Climax I and II experiments indicates that the evidence for a seeding effect is a little stronger (i.e., smaller P values) with the new technique than with the old technique for the warm Climax II 500 mb temperature partition and a Climax I 700 mb wind velocity partition, a little weaker for the warm Climax II 700 mb equivalent potential temperature partition, and about the same for the other meteorological partitions examined.
Abstract
This paper considers the examination of possible differences in monthly sea-level pressure patterns, The satisfactory examination of such differences requires appropriate multi-response parametric methods based on unknown multivariate distributions (i.e., an appropriate parametric technique is probably non-existent). In order to avoid the likely insurmountable difficulties involving parametric methods, the application of multi-response permutation procedures (MRPP) is suggested as an appropriate approach for the examination of such differences.
Abstract
This paper considers the examination of possible differences in monthly sea-level pressure patterns, The satisfactory examination of such differences requires appropriate multi-response parametric methods based on unknown multivariate distributions (i.e., an appropriate parametric technique is probably non-existent). In order to avoid the likely insurmountable difficulties involving parametric methods, the application of multi-response permutation procedures (MRPP) is suggested as an appropriate approach for the examination of such differences.
Abstract
An estimator of shrinkage based on information contained in a single sample is presented and the results of a simulation study are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations and least (sum of) squared deviations regression models are examined on the estimator. A single-sample estimator of shrinkage based on drop-one cross-validation is shown to be highly accurate under a wide variety of research conditions.
Abstract
An estimator of shrinkage based on information contained in a single sample is presented and the results of a simulation study are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations and least (sum of) squared deviations regression models are examined on the estimator. A single-sample estimator of shrinkage based on drop-one cross-validation is shown to be highly accurate under a wide variety of research conditions.
Abstract
Western Sahelian rainfall during the primary rainy season of June through September is shown to he significantly associated with concurrent intense U.S. landfalling hurricanes during the last 92 years. The meet intense hurricanes (i.e., Saffir–Simpson Scale Category 3, 4, or 5) have an especially strong relationship with Sahelian rainfall, whereas weaker hurricanes show little or no association. The hurricane-Sahelian rainfall association is most evident along the U.S. East Coast but is negligible in the U.S. Gulf Coast region.
Abstract
Western Sahelian rainfall during the primary rainy season of June through September is shown to he significantly associated with concurrent intense U.S. landfalling hurricanes during the last 92 years. The meet intense hurricanes (i.e., Saffir–Simpson Scale Category 3, 4, or 5) have an especially strong relationship with Sahelian rainfall, whereas weaker hurricanes show little or no association. The hurricane-Sahelian rainfall association is most evident along the U.S. East Coast but is negligible in the U.S. Gulf Coast region.
Abstract
More than 90% of all seasonal Atlantic tropical cyclone activity typically occurs after 1 August. A strong predictive potential exists that allows seasonal forecasts of Atlantic basin tropical cyclone activity to be issued by 1 August, prior to the start of the active portion of the hurricane season. Predictors include June-July meteorological information of the stratospheric quasi-biennial oscillation (QBO), West African rainfall, the El Niño-Southern Oscillation (ENSO) as well as sea level pressure anomalies (SLPA), and the upper-tropospheric zonal-wind anomalies (ZWA) in the Caribbean basin.
Use of a combination of these global and regional predictors provides a basis for making cross-validated (jackknifed) 1 August hindcasts of subsequent Atlantic seasonal tropical cyclone activity that show substantial skill over climatology. This relationship is demonstrated in 41 years of hindcasts of the 1950-90 seasons. It is possible to independently explain more than 60% of the year-to-year variability associated with intense (category 3–4–5) hurricane activity. This is significant because over 70% of all United States tropical cyclone damage comes from intense hurricanes, and over 98% of intense hurricane activity occurs after 1 August.
Empirical evidence suggests that least sum of absolute deviations (LAD) regression yields substantially more improved cross-validated results than an analogous procedure based on ordinary least sum of squared deviations (OLS) regression. This improvement surprisingly occurs even with the squared Pearson product-moment correlation coefficient for which one might anticipate OLS regression to yield better cross-validated results than LAD regression.
Abstract
More than 90% of all seasonal Atlantic tropical cyclone activity typically occurs after 1 August. A strong predictive potential exists that allows seasonal forecasts of Atlantic basin tropical cyclone activity to be issued by 1 August, prior to the start of the active portion of the hurricane season. Predictors include June-July meteorological information of the stratospheric quasi-biennial oscillation (QBO), West African rainfall, the El Niño-Southern Oscillation (ENSO) as well as sea level pressure anomalies (SLPA), and the upper-tropospheric zonal-wind anomalies (ZWA) in the Caribbean basin.
Use of a combination of these global and regional predictors provides a basis for making cross-validated (jackknifed) 1 August hindcasts of subsequent Atlantic seasonal tropical cyclone activity that show substantial skill over climatology. This relationship is demonstrated in 41 years of hindcasts of the 1950-90 seasons. It is possible to independently explain more than 60% of the year-to-year variability associated with intense (category 3–4–5) hurricane activity. This is significant because over 70% of all United States tropical cyclone damage comes from intense hurricanes, and over 98% of intense hurricane activity occurs after 1 August.
Empirical evidence suggests that least sum of absolute deviations (LAD) regression yields substantially more improved cross-validated results than an analogous procedure based on ordinary least sum of squared deviations (OLS) regression. This improvement surprisingly occurs even with the squared Pearson product-moment correlation coefficient for which one might anticipate OLS regression to yield better cross-validated results than LAD regression.
Abstract
This is the third in a series of papers describing the potential for the seasonal forecasting of Atlantic basin tropical cyclone activity. Earlier papers by the authors describe seasonal prediction from 1 December of the previous year and from 1 August of the current year; this work demonstrates the degree of predictability by 1 June, the “official” beginning of the hurricane season. Through three groupings consisting of 13 separate predictors, hindcasts are made that explain 51%–72% of the variability as measured by cross-validated agreement coefficients for eight measures of seasonal tropical cyclone activity. The three groupings of predictors include 1) an extrapolation of quasi-biennial oscillation of 50- and 30-mb zonal winds and the vertical shear between the 50- and 30-mb zonal winds (three predictors); 2) West African rainfall, sea level pressure, and temperature data (four predictors); and 3) Caribbean basin and El Niño–Southern Oscillation information including Caribbean 200-mb zonal winds and sea level pressures, equatorial eastern Pacific sea surface temperatures and Southern Oscillation index values, and their changes in time (six predictors). The cross validation is carried out using least sum of absolute deviations regression that provides an efficient procedure for the maximum agreement measure criterion. Corrected intense hurricane data for the 1950s and 1960s have been incorporated into the forecasts. Comparisons of these 1 June forecast results with forecast results from 1 December of the year previous and 1 August of the current year are also given.
Abstract
This is the third in a series of papers describing the potential for the seasonal forecasting of Atlantic basin tropical cyclone activity. Earlier papers by the authors describe seasonal prediction from 1 December of the previous year and from 1 August of the current year; this work demonstrates the degree of predictability by 1 June, the “official” beginning of the hurricane season. Through three groupings consisting of 13 separate predictors, hindcasts are made that explain 51%–72% of the variability as measured by cross-validated agreement coefficients for eight measures of seasonal tropical cyclone activity. The three groupings of predictors include 1) an extrapolation of quasi-biennial oscillation of 50- and 30-mb zonal winds and the vertical shear between the 50- and 30-mb zonal winds (three predictors); 2) West African rainfall, sea level pressure, and temperature data (four predictors); and 3) Caribbean basin and El Niño–Southern Oscillation information including Caribbean 200-mb zonal winds and sea level pressures, equatorial eastern Pacific sea surface temperatures and Southern Oscillation index values, and their changes in time (six predictors). The cross validation is carried out using least sum of absolute deviations regression that provides an efficient procedure for the maximum agreement measure criterion. Corrected intense hurricane data for the 1950s and 1960s have been incorporated into the forecasts. Comparisons of these 1 June forecast results with forecast results from 1 December of the year previous and 1 August of the current year are also given.
Abstract
The results of a simulation study of multiple regression prediction models for meteorological forecasting are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations (LAD) and least (sum of) squared deviations (LSD) regression models are examined on five populations constructed from meteorological data. Artificial skill is shown to be a product of small sample size, LSD regression, and nonrepresentative data. Validation of sample results is examined, and LAD regression is found to be superior to LSD regression when sample size is small and nonrepresentative data are present.
Abstract
The results of a simulation study of multiple regression prediction models for meteorological forecasting are reported. The effects of sample size, amount, and severity of nonrepresentative data in the population, inclusion of noninformative predictors, and least (sum of) absolute deviations (LAD) and least (sum of) squared deviations (LSD) regression models are examined on five populations constructed from meteorological data. Artificial skill is shown to be a product of small sample size, LSD regression, and nonrepresentative data. Validation of sample results is examined, and LAD regression is found to be superior to LSD regression when sample size is small and nonrepresentative data are present.
Abstract
A surprisingly strong long-range predictive signal exists for Atlantic-basin seasonal tropical cyclone activity. This predictive skill is related to two measures of West African rainfall in the prior year and to the phase of the stratospheric quasi-biennial oscillation of zonal winds at 30 mb and 50 mb, extrapolated ten months into the future. These predictors, both of which are available by 1 December, can be utilized to make skillful forecasts of Atlantic tropical cyclone activity in the following June-November season. Using jackknife methods to provide independent testing of datasets, it is found that these parameters can be used to forecast nearly half of the season-to-season variability for seven indices of Atlantic seasonal tropical cyclone activity as early as late November of the previous year.
Abstract
A surprisingly strong long-range predictive signal exists for Atlantic-basin seasonal tropical cyclone activity. This predictive skill is related to two measures of West African rainfall in the prior year and to the phase of the stratospheric quasi-biennial oscillation of zonal winds at 30 mb and 50 mb, extrapolated ten months into the future. These predictors, both of which are available by 1 December, can be utilized to make skillful forecasts of Atlantic tropical cyclone activity in the following June-November season. Using jackknife methods to provide independent testing of datasets, it is found that these parameters can be used to forecast nearly half of the season-to-season variability for seven indices of Atlantic seasonal tropical cyclone activity as early as late November of the previous year.
Abstract
Results of statistical analyses for HIPLEX-1, a randomized cloud seeding experiment, are presented. The analyses are based principally on multi-response permutation procedures (MRPP) as specified before the HIPLEX-1 experiment was initiated. Even though the sample sizes are very small, due in part to the premature termination of this experiment, the three primary response variables measured in the first five minutes following treatment indicate pronounced differences in the development of ice crystals between nonseeded and seeded events. However, the response variables measured more than five minutes after treatment generally do not indicate obvious differences in the subsequent development of precipitation between nonseeded and seeded events. This lack of difference is a possible consequence of 1) lack of a seeding effect, 2) inadequacies in the physical hypothesis, or 3) the small sample sizes. Consequently, only the initial steps in the HIPLEX-1 physical hypothesis could be confirmed in this evaluation of the experiment.
Abstract
Results of statistical analyses for HIPLEX-1, a randomized cloud seeding experiment, are presented. The analyses are based principally on multi-response permutation procedures (MRPP) as specified before the HIPLEX-1 experiment was initiated. Even though the sample sizes are very small, due in part to the premature termination of this experiment, the three primary response variables measured in the first five minutes following treatment indicate pronounced differences in the development of ice crystals between nonseeded and seeded events. However, the response variables measured more than five minutes after treatment generally do not indicate obvious differences in the subsequent development of precipitation between nonseeded and seeded events. This lack of difference is a possible consequence of 1) lack of a seeding effect, 2) inadequacies in the physical hypothesis, or 3) the small sample sizes. Consequently, only the initial steps in the HIPLEX-1 physical hypothesis could be confirmed in this evaluation of the experiment.
Abstract
The design and conduct of HIPLEX-1, a randomized seeding experiment carried out on small cumulus congestus clouds in eastern Montana, are outlined. The seeding agent was dry ice, introduced in an effort to produce microphysical effects, especially the earlier formation of precipitation in the seeded clouds. The earlier formation was expected to increase both the probability and the amount of precipitation from those small clouds with short lifetimes. The experimental unit selection procedure, treatment and randomization procedures, the physical hypothesis, measurement procedures and the response variables defined for the experiment are discussed. Procedures used to calculate the response variables from aircraft and radar measurements are summarized and the values of those variables for the 20 HIPLEX-1 test cases from 1979 and 1980 are tabulated.
Abstract
The design and conduct of HIPLEX-1, a randomized seeding experiment carried out on small cumulus congestus clouds in eastern Montana, are outlined. The seeding agent was dry ice, introduced in an effort to produce microphysical effects, especially the earlier formation of precipitation in the seeded clouds. The earlier formation was expected to increase both the probability and the amount of precipitation from those small clouds with short lifetimes. The experimental unit selection procedure, treatment and randomization procedures, the physical hypothesis, measurement procedures and the response variables defined for the experiment are discussed. Procedures used to calculate the response variables from aircraft and radar measurements are summarized and the values of those variables for the 20 HIPLEX-1 test cases from 1979 and 1980 are tabulated.
